WO1992013308A1 - Systeme et procede de classification morphologique - Google Patents

Systeme et procede de classification morphologique Download PDF

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Publication number
WO1992013308A1
WO1992013308A1 PCT/US1992/000660 US9200660W WO9213308A1 WO 1992013308 A1 WO1992013308 A1 WO 1992013308A1 US 9200660 W US9200660 W US 9200660W WO 9213308 A1 WO9213308 A1 WO 9213308A1
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WIPO (PCT)
Prior art keywords
image
objects
representation
images
cell
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PCT/US1992/000660
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English (en)
Inventor
Randall L. Luck
Richard Scott
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Neuromedical Systems, Inc.
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Application filed by Neuromedical Systems, Inc. filed Critical Neuromedical Systems, Inc.
Publication of WO1992013308A1 publication Critical patent/WO1992013308A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G06V20/693—Acquisition
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N15/1468—Optical investigation techniques, e.g. flow cytometry with spatial resolution of the texture or inner structure of the particle
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N15/1429—Signal processing
    • G01N15/1433—Signal processing using image recognition
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S706/00—Data processing: artificial intelligence
    • Y10S706/902—Application using ai with detail of the ai system
    • Y10S706/924—Medical

Definitions

  • This invention relates generally to classification, particularly to cytology, and, more particularly, to a method and apparatus for quickly and accurately classifying
  • a pap smear often contains as many as 100,000 to 200,000 or more cells and other objects, each of which a technician must individually inspect in order to determine the possible presence of very few
  • classifier is produced by Neuromedical Systems, Inc.* of Suffern, New York under
  • the present invention provides a method and apparatus for automating a cell classification process using at least primary and secondary classifications.
  • the primary classification step includes performing morphological
  • SUBSTITUTE SHEET secondary classification step then further classifies these objects using an implementation of a neural network.
  • the present invention preferably performs at least two scans of the specimen to produce one image suitable for analysis by the primary and secondary classifiers and a second image suitable for display on a high
  • predetermined criteria obtaining a second image of at least one of the objects most likely to have a predetermined criteria, and displaying at least part of the second image to produce a visual display of at least one of the objects most likely to have a predetermined criteria.
  • a method of classifying objects in a specimen includes the steps of obtaining a first digital representation of at least part of the cytological specimen, storing the first digital representation,
  • an apparatus for classifying objects in a cytological specimen includes a device for obtaining a first image of at least part of the cytological specimen, a processor for classifying objects in the first image on the basis of a predetermined criteria, a device for obtaining a second image of at least one of the objects most likely to have a predetermined
  • a monitor for displaying at least part of the second image to produce a visual display of at least one of the objects most likely to have the predete ⁇ nined criteria.
  • Figure 1 is a schematic illustration of a cytological classification or screening
  • SUBSTITUTE SH Figure 2 is a diagrammatic illustration of the scanning passes which the screening device performs
  • FIG 3 is a schematic illustration of the screening device of Figure 1 with particular emphasis on the processing system;
  • Figure 4 is an illustration of the various image components representing areas of the specimen
  • Figures 5a through 5c are flowcharts illustrating the primary classification
  • Figures 6a through 6d are graphical representations of a morphological closing
  • the device 10 includes
  • an automated optical microscope 12 having a motorized stage 14 for the movement of a slide 16 relative to the viewing region of the viewing portion 18 of the
  • a-camera 2 ⁇ or Obtaining electronic images from the optical microscope, a processing system 22 for classifying objects in the images as likely to be of a predetermined cell type, and a memory 24 and a high resolution color monitor 26 for
  • the storage and display respectively of objects identified by the processing system as being likely to be of that predetermined cell type.
  • the classification device 10 is completely, or
  • the microscope 12 will preferably include, in addition to the motorized stage 14, automated apparatus for focusing, for changing
  • the microscope may also include an automated slide transport system for moving the slides containing the specimen to be classified on to and off of the motorized stage, a cell juryer for
  • the automated microscope 12 preferably performs three scans of the slide having the specimen disposed thereon, as shown diagrammatically in Figure 2.
  • the first scan of the slide is performed at a relatively low magnification, for example 50 power, and is called the low resolution scan (30).
  • the second scan is performed at a higher magnification, for example 200 power, and is called the high resolution scan (35).
  • the third scan is referred to as the high resolution rescan and is also performed at a high magnification (40).
  • the high resolution scan (35) is performed.
  • the high resolution scan (35) is performed only on the areas of the slide found in the low resolution scan (30) to contain a portion of the specimen. Consequently,
  • the comparatively long high resolution scan (35) is performed only on relevant areas of the slide and the processing time is greatly reduced.
  • SUBSTITUTE SHEET scan (35), the automated microscope 12 scans the relevant areas of the slide, and the camera 20 takes electronic images of these areas and sends the images to the processing system 22.
  • the processing system performs a primary classification of the image which finds the centroids of biological objects having attributes typical of
  • the processing system 22 uses a smaller sub-image centered around these centroids to assigns each centroid a value indicative of
  • centroid is a cell of the type for which classification is being performed.
  • centroids are also ranked based
  • the high resolution rescan (40) is performed for the highest 64 ranked objects.
  • the automated microscope 12 will move to each of the highest 64 ranked centroids and the camera 20 will obtain a high resolution color image of the object having that centroid.
  • These 64 high resolution images, called color tiles, are then stored in the
  • memory 24 which may be a removable device, such as an optical disk or a tape, etc. , or a fixed storage-device-such ⁇ s hard disk.
  • the sixty-four color tiles may be transferred to another computer via a network or through transportation of the data on a removable storage media.
  • the sixty-four color tiles make up a summary screen which is preferably an 8 x 8 matrix of high resolution color tiles featuring a suspect cell in the center of each
  • SUBSTITUTE SHEET analysis and classification by a cytotechnician This analysis may take place at anytime after the highest sixty-four have been secondarily classified and ranked. Further, through the use of a removable memory device or a network connection, the images and tiles of the summary screen may be transferred to a workstation remote from the microscope 18, camera 20 and processing system 22 for display and analysis. In such an instance a separate graphics processor 41 ( Figure 3) may be employed to drive the high resolution color monitor 26 and provide a suitable interface with the cytotechnician.
  • a cytotechnician or cytotechnologist can easily scan the summary screen in search of an object having the attributes of the cell type for which classification is being performed. If the system is being used ' to screen a pap smear for the presence of cervical cancer, the cytotechnician would typically look for cells having attributes of malignant or premalignant cervical cells, such as a comparatively large, dark nucleus. The cytotechnician may also make a determination from the summary screen as to whether a specimen having no
  • endocervic-d ⁇ c ⁇ ls make up me -ining of the transitional zone of the cervix where most cervical cancers start; consequently, their presence in a pap smear specimen indicates that the test swab must have made contact with the critical transitional zone of the cervix. Since endocervical cells have more characteristics common to a malignant cell than a vaginal or other benign cell has,
  • the screening may be performed for the detection of other cell classes or types.
  • the screening device 10 is shown with particular emphasis on the classification elements embodied in the processing system 22.
  • the processing system 22 preferably includes an image processor and digitizer 42, a neurocomputer 44, and a general processor 46 with peripherals for printing, storage, etc.
  • the general processor 46 is preferably an Intel* 80386 microprocessor or faster microprocessor based microcomputer although it may be another computer-type device suitable for efficrent-e-ircratio ⁇ thei ⁇ -mcti ⁇ ns xiescribed herein.
  • the general processor 46 is preferably an Intel* 80386 microprocessor or faster microprocessor based microcomputer although it may be another computer-type device suitable for efficrent-e-ircratio ⁇ thei ⁇ -mcti ⁇ ns xiescribed herein.
  • processor 46 controls the functioning of and the flow of data between components of
  • the device 10 may cause execution of additional primary feature extraction algorithms and handles the storage of image and classification information.
  • the general processor 46 additionally controls peripheral devices such as a printer 48, a storage device 24 such as an optical or magnetic hard disk, a tape drive, etc., as well as other devices such as a bar code reader 50, a slide marker 52, autofocus circuitry, a robotic slide handler, and the stage 14.
  • the image processor and digitizer 42 performs the primary cell classification functions described more fully below.
  • the image processor and digitizer 42 is a commercially available low level morphological feature extraction image classifier such as the ASPEX Incorporated PIPE* image processor which includes among other things an image digitization function and an ISMAP (Iconic to Symbolic Mapping) board.
  • the PIPE ® image processor is described more fully in U.S. Patent No. 4,601,055, the entire disclosure of which is incorporated by this reference.
  • the image processing and digitization functions could be separated into two or more components.
  • the image processor and digitizer will be conjunctively referred to as the image processor 42.
  • the neurocomputer 44 is a computer embodiment of a neural network trained to identify suspect cells.
  • backpropagation neural network is emulated with pipelined serial processing techniques executed on one of a host of commercially available neurocomputer accelerator boards.
  • the operation of these neurocomputers is discussed in Hecht- Nielsen, -Robert, • *Neu ⁇ uc ⁇ mpu-ing. ⁇ ⁇ id ⁇ g ⁇ the -Human Brain*, IEEE Spectrum, March, 1988, pp. 36-41.
  • the neural network is preferably implemented on an Anza PlusTM processor, which is a commercially available neurocomputer of Hecht-Nielsen
  • Neurocomputers Such a neurocomputer could be easily configured to operate in a manner suitable to perform the secondary classification functions by one of ordinary
  • secondary cell classification functions could be performed using a template matching
  • Another alternative secondary classification embodiment is a holographic image processor
  • the image processor 42, the neurocomputer 44, and the general computer 46 may each access read-only and/or random access memory, as would be readily apparent to one skilled in the art, for the storage and execution of software necessary to perform the functions described relative to that processing component. Further, each component 42, 44, 46 includes circuitry, chips, etc. for the control of
  • the area of the slide 16 possibly containing the biological matter of the specimen is segmented into a plurality of rows and columns, for example, 20 rows
  • Each block 60 occupies an area of the slide, for example, approximately 2000 microns x 1600 microns, and corresponds to an individual image to be viewedt e by g one
  • Each block 60 is subdivided, for example, into sixteen equally sized analysis fields 62. Each field 62 is thus approximately 500 microns by 400 microns in size.
  • each analysis field 62 will be represented by a 256 by 242 matrix or array of pixels which corresponds to a resolution of approximately two microns per pixel during a low resolution scan (30) or high resolution scan (35), or a 512 by
  • Each pixel then represents the brightness, or gray scale
  • SUBSTITUTE SHEET density of a discrete area of the analysis field 62 image The gray scale density of each pixel is further represented by an 8 bit digital value. Consequently, each pixel will represent an area of the analysis field image 62 by a gray scale level ranging from zero to 255.
  • the screening device will perform a low resolution scan (30) on each analysis field 62 to determine if that field contains biological matter, and a high resolution scan (35) on each of the analysis fields 62 having biological matter to detect objects contained therein which are likely to be malignant
  • a third scan (40), the high resolution rescan, may also be performed on an analysis field 62, or a portion of an analysis field, if during the high resolution scan (35) the processing system found an object within the field which is likely to be a malignant or premalignant cell.
  • the objective of the microscope 12 is set, for example, at its 50 magnification power, and the microscope begins scanning the individual blocks 60 of the slide 16. For each block 60 the microscope 12 will automatically determine the approximate focal plane for that area of the slide 16.
  • the cover slip covering the specimen tends to be somewhat wavy or possibly angled
  • the focal plane may vary from block 60 to block.
  • the camera 20 will capture the image of the block and send that image to the image processor 42 through a suitable digitizer.
  • the image processor 42 then subdivides the block 60 into analysis field 62 and determines whether there are areas of interest
  • each analysis field corresponding to objects which may be biological material. If a field 62 contains material which may be biological, the block 60 is identified along
  • the high resolution scan (35) is begun. Initially, a scan path is determined which will allow the microscope 12 to view each block 60 possibly containing
  • the objective corresponding, for example, to a 200 power magnification is inserted into the viewing path of the microscope 12, and the
  • the microscope 12 via the motorized stage 14 will move the slide 16 into a position such that the first block 60,
  • the microscope 12 will then, based initially on the focal plane determined during the low resolution scan (30), focus the block 60 under the high resolution magnification level.
  • the block 60 is digitized and again subdivided into 16 analysis fields 62.
  • the image processor 42 will then
  • This primary classification finds the centroids of objects in each field that have the correct size and gray scale density characteristics.
  • a net image 64 is approximately
  • SUBSTITUTE SHEET premalignant cell nucleus tends to range between 10 and 40 microns in diameter, the net image 64 is sufficiently large to contain a complete image of a suspect cell.
  • the highest 64 ranked objects are displayed on the summary screen 66.
  • the summary screen may be an 8 x 8 matrix of 64 discrete images, called color tiles 68, a 4 4 arrangement of 16 color tiles, or some other arrangement.
  • Color tiles 68 are obtained during the rescan pass (40).
  • Each color tile represents an approximately 128 x 104 micron area surrounding the centroid of a suspect cell, with a resolution of one micron per pixel.
  • Each color tile produces a high resolution color image of a suspect cell and surrounding cells and biological matter, with the suspect cell centered in the tile.
  • magnification levels, etc. can be employed to accomplish the same or similar results --s thep--rt--etriarembodmtent of theinvention described above, and that all such differing resolutions, image parameters, etc. are within the scope of the present invention.
  • the Papanicolaou stain used in treating a pap smear dyes the nuclei of biological cells within the smear a
  • the primary classifier performs a morphological "well” algorithm which filters out objects that are the size of a premalignant or malignant cell or smaller.
  • a "well” algorithm is the inverse of a morphological "top hat” algorithm.
  • the resulting image containing only objects which are too large to be a cancerous cell, is then subtracted from the original image containing all of the objects.
  • the centroids of the objects in this image are then determined and the images centered around those centroids are sent to the secondary classifier for further classification.
  • the image in the frame buffer which corresponds to an analysis field 62 and is referred to herein as the frame image, is spatially filtered such as with a gaussian low pass filter to remove random noise from the image (110).
  • the gaussian filter has a convolution mask of:
  • This convolution mask is moved across every pixel in the frame image. To explain, the convolution mask will be initially centered on the first pixel in the frame image
  • Gray scale dilation is a mathematical morphology term used in image processing to denote an operation wherein a mask is centered on a pixel and a corresponding pixel in a corresponding result frame pixel is replaced by the largest value of its neighboring pixels added to their corresponding mask values or itself added to its corresponding mask value.
  • Erosion is a similar term wherein the center pixel is replaced by the minimum value
  • a gray scale erosion operation is first performed using a 4 neighboring pixel mask followed by an erosion using an 8 neighboring pixel mask
  • the object on the left 70 is a large dark nucleus and the object on the right 72 is a smaller, less dark cell, such as a leukocyte.
  • the horizontal line 74 represents a row of pixels passing through the objects 70, 72.
  • the gray scale values for this row of pixels is shown in Figure 6b.
  • the large dark nucleus 70 forms a wide and deep gray scale rectangle 76 due to its relatively large size and darkness.
  • the leukocyte 70 being smaller and less dark, forms a narrower, shallower rectangle 78.
  • nucleus may now be represented by the relatively wide depression 80 while the leukocyte may be represented by the narrow spike 82. Subsequent dilation operations
  • a gray scale dilation is performed on the gaussian low pass filtered frame image using an 8 neighboring pixel mask (115).
  • a gray scale dilation is then performed on this dilated frame image using a 4 neighboring pixel mask (120).
  • This series of gray scale dilations is then performed one additional time (125, 130).
  • the dilated image is then eroded using a 4 neighboring pixel mask erosion followed by an 8 neighboring pixel mask erosion (160). Again, this series of erosions
  • SUBSTITUTE SHEET frame image thus consists only of objects which generally correspond to the size of a malignant or premalignant nucleus or smaller.
  • a threshold operation is then performed on this image with pixels having a gray scale value above a certain threshold being assigned a binary ' 1 ' and those pixels have gray scale values below that threshold being assigned a binary zero (200).
  • the threshold is chosen to filter out objects which are not dark enough to be nuclei.
  • a binary 3 x 3 erosion is then performed on the image to remove the outer pixel
  • This binary erosion is accomplished by performing a boolean AND on the center pixel and the eight neighboring pixels. Representing the mask as below:
  • the frame image now contains objects of the appropriate gray scale density that are of a size corresponding to a malignant or premalignant cervical cell and
  • a binary mask is needed to mask off the smaller objects. Accordingly, the mask would have binary 'l's in all locations of the frame image pixel array except for those locations occupied by a small image, where there would be zeros. Consequently, by boolean ANDing the binary frame image with the binary
  • This mask is obtained by taking the untreated frame image (C) stored earlier
  • binary dilation is then performed on the image to slightly expand or thicken the edges in the image (235).
  • the binary dilation operation is performed as a boolean 'OR' operation.
  • This function could also be performed with a gray-scale morphological well or top hat filter as described above with its parameters set to find smaller objects.
  • a number of the outermost rows and columns of pixels, for example, eight, forming a border around the frame are then removed, as they contain artifacts and other irrelevant information introduced by the operations performed above (245).
  • the complement of the resultant image is then taken by a boolean 'NOT' operation (250). Consequently, the binary image will consist of binary 'l's in all locations except in the slightly enlarged areas encompassing objects too small to be malignant cells. This complemented image thus forms the binary mask used to subtract the small objects from the earlier developed frame image.
  • the suppression operation can be any combination of binary erosion and dilation operations.
  • SUBSTITUTE SHEET be expressed mathematically as (E & A) ! (E & B)
  • the shrinking operation successively removes layers of pixels around the object in the image frame until, for an object of the appropriate size, only one pixel or no pixels remain. In the case where the object is completely removed, the last pixel removed is replaced with a binary 1. Since the outer layers of pixels of the objects were successively removed progressing inwardly,
  • the remaining or replaced pixel will represent the approximate center or centroid of the object. Any objects remaining in the image which are larger than one pixel are
  • the identification of the centroids ⁇ f objects ⁇ vhfcfr * correspond to the same size and gray scale density that a typical cervical cancer cell would be expected to have marks the end of the primary classification phase of operation.
  • the code would be loaded, burned, or otherwise encoded into memory accessible by the image processor 42 for execution by the image processor.
  • the general processor 46 individually transfers each net image 64 to the
  • the task of the secondary classification is to distinguish the premalignant and malignant cells from other objects of the same size which may pass the primary classifier, such as cell clumps, debris, clumps of leukocytes and mucus.
  • the neurocomputer 44 will assign each net image 64 with a value, called a net value, ranging from .1 to .9, as determined by the likelihood that the object is a premalignant or malignant cell.
  • a net value ranging from .1 to .9, as determined by the likelihood that the object is a premalignant or malignant cell.
  • the present invention overcomes this difficulty in the domain of cytology.
  • Another advantage of the present invention is that during actual classification operations, a secondary classifier is presented with precisely the same type of net images on which it was trained. These images are also centered on the centroid of the suspect nucleus by the primary classifier in a manner identical to that used to prepare the training set images. This makes a generalization task of the secondary classifier
  • the secondary classifier is trained to associate a known benign image with an output of .1 and a known pathological image with an output of .9. Such outputs represent, for example, the degree of certainty that a cell is normal or abnormal, respectively.
  • the net values assigned to those objects by the secondary classifier are ranked from closest to .9 down to .1.
  • the high resolution rescan (40) is begun at the 200 power magnification.
  • the stage 14 will move the slide relative to the microscope 12 so that one of the highest 64 ranked object is in the viewing path of the microscope.
  • the image is then focused according to the previously determined high resolution focus parameters, and the camera grabs, at 512 x 484 resolution, the 128 x 104 red, green and blue component image around the centroid location.
  • This high resolution color tile 68 is then stored in the memory 24, such as on an optical disk or tape. These operations are then performed for the next cell until all 64 of the highest ranked cells have been rescanned and their high resolution color images have been stored in the memory 24. This completes the
  • the automated classifier 10 may then remove the slide and replace it with another slide for further classification.
  • the 64 color tiles 68 may be displayed as a summary screen 66 in their descending order of ------iking, their positioned relation to each other in the
  • the present invention is applicable to cell classification in general and is particularly applicable to the classification of cells in a pap smear.

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Abstract

Un procédé de classification d'objets dans un échantillon comprend les étapes consistant à obtenir une première représentation numérique d'au moins une partie de l'échantillon cytologique, à stocker la première représentation numérique, à procéder à une première opération de filtrage afin d'éliminer les images se trouvant dans la première représentation et de la taille approximative d'une cellule maligne ou prémaligne ou d'une taille inférieure afin de produire une seconde représentation numérique, à retirer les images se trouvant dans la seconde représentation des images de la première représentation afin de produire une troisième représentation, à procéder à une seconde opération de filtrage afin d'éliminer les images se trouvant dans la première représentation et qui sont plus petites que la taille approximative d'une cellule prémaligne ou maligne afin de produire une quatrième représentation, et à éliminer les images se trouvant dans la quatrième représentation des images de la troisième représentation afin de produire une représentation comportant uniquement des images de la taille approximative d'une cellule prémaligne ou maligne.
PCT/US1992/000660 1991-01-29 1992-01-28 Systeme et procede de classification morphologique WO1992013308A1 (fr)

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US07647438 US5257182B1 (en) 1991-01-29 1991-01-29 Morphological classification system and method
US647,438 1991-01-29

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US5257182A (en) 1993-10-26
US5257182B1 (en) 1996-05-07

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